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Found 7 papers, 0 papers with code
Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images
In this work, we built an automatic image-understanding method that can accurately classify different types of colorectal polyps in whole-slide histology images to help pathologists with histopathological characterization and diagnosis of colorectal polyps.
Automated detection of celiac disease on duodenal biopsy slides: a deep learning approach
In this study, we trained a deep learning model to detect celiac disease on duodenal biopsy images.
Deep neural networks for automated classification of colorectal polyps on histopathology slides: A multi-institutional evaluation
An accurate and automated algorithm for the classification of colorectal polyps on digitized histopathology slides could benefit clinicians and patients.
Predicting colorectal polyp recurrence using time-to-event analysis of medical records
Identifying patient characteristics that influence the rate of colorectal polyp recurrence can provide important insights into which patients are at higher risk for recurrence.
Resolution-Based Distillation for Efficient Histology Image Classification
Our results show that a combination of KD and self-supervision allows the student model to approach, and in some cases, surpass the classification accuracy of the teacher, while being much more efficient.
MHAttnSurv: Multi-Head Attention for Survival Prediction Using Whole-Slide Pathology Images
In pathology, whole-slide images (WSI) based survival prediction has attracted increasing interest.
Masked Pre-Training of Transformers for Histology Image Analysis
However, due to the large number of model parameters and limited labeled data, applying transformer models to WSIs remains challenging.
